M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience Strong foundation in deep learning theory and hands-on experience training large-scale models Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow/JAX Hands-on experience with model compression and optimization techniques (quantization, pruning, distillation, etc.) Familiarity with on-device inference frameworks such as Core ML, TensorFlow Lite, ONNX Runtime, or TensorRT Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion) Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code Experience deploying models to embedded systems, mobile devices, or custom silicon (NPU/DSP) Familiarity with hardware-aware neural architecture search (NAS) or AutoML techniques Exposure to low-level optimization techniques such as mixed-precision training or operator fusion Hands-on experience with Apple Neural Engine and Core ML for on-device inference Publications or open-source contributions in efficient deep learning or edge AI Experience with MLOps workflows and CI/CD pipelines for model development
